Use of the cross-section technique linked with multivariate calibration methods to resolve complex pesticide mixtures.

The potential of the cross-section (CS) approach in combination with the partial least squares (PLS) and principal component regression (PCR) was assessed in the resolution of a complex pesticide mixture showing twelve overlapped components in High Performance Liquid Chromatography with Diode Array Detection (HPLC-DAD). Careful selection of the CS through the three-dimensional (3D) (A, lambda, t) data matrix gave two-dimensional (2D) signals with the best sensitivity for the determination of each pesticide. In all cases, the application of the PLS method demonstrated a better quantitative prediction ability than that of the PCR method. The CS-PLS approach is a powerful analytical tool. Ten pesticides were well-resolved, while for the other two pesticides of the mixture prediction ability was poor, and they could not be determined, probably due to their low net analytical signal. The CS-PLS model was evaluated by predicting the concentrations of independent test set samples. Finally, the proposed model was successfully applied for the determination of these pesticides in groundwater.